About
Vaiyu — वायु — is Sanskrit for wind.
Unseen, everywhere, load-bearing. That’s how we think about AI in serious organizations: not the demo on stage, but the infrastructure underneath it — quiet, reliable, doing its work. Vaiyu Solutions was founded to build exactly that.
Who we are
Small by design.
Founded in October 2023 and based in Worldwide, Vaiyu Solutions is an interdisciplinary team of five engineers and scientists: machine learning, distributed systems, data engineering, and domain specialists in medical imaging and regulated deployment.
Senior people, end-to-end accountability, no bench. We work remote-first with clients worldwide, and we stay small on purpose — the people who scope your problem are the people who ship it.
How we work
Four commitments.
01
Rigor over hype
Every number on this site carries a citation. Client work gets the same discipline: measured, validated, reproducible.
02
End-to-end ownership
We don’t hand off at the slide deck. Architecture, data, training, deployment, monitoring — one accountable team.
03
Knowledge transfer
Engagements end with your team stronger. Documentation, training, and handover are deliverables, not afterthoughts.
04
Built for regulated reality
Healthcare taught us to build for auditors, ethics boards, and six-year lifecycles. Every industry deserves that discipline.
Leadership
Sarthak Pati
Founder & CEO
Sarthak holds a Ph.D. in Computer Science from the Technical University of Munich (summa cum laude) and has spent 11+ years operationalizing AI in clinical environments — eight of them at the University of Pennsylvania building imaging AI used across hospital networks, then as software architect at Indiana University. Along the way he has led more than $9M in NIH/NCI-funded R&D.
He serves as Vice Chair for Algorithmic Development of theMLCommons Medical Working Group, created GaNDLF, co-led CaPTk, FeTS, and MedPerf, and has taught federated learning at MICCAI, AAAI, ISBI, and RSNA. His research appears in Nature Communications and Nature Machine Intelligence and has been covered by The Wall Street Journal.
Put the team to work.
We typically start with a 2–4 week discovery sprint: framing, feasibility, and a costed plan.